The present invention relates to a method for analyzing a multispectral image, and to a computer program for implementing such a method. It also relates to a method and a device for the surveillance of an environment.
The surveillance of an environment is a common task, particularly for intrusion detection. Such surveillance presents particular difficulties when carried out in a terrestrial environment. A terrestrial environment such as a rural area may contain a large number of different elements with irregular contours such as trees, bushes, rocks, buildings, etc., which makes it complex to interpret an image and search for intruding elements. In addition, in certain circumstances such as military surveillance, an intruding element may be camouflaged to make it more difficult to detect in the landscape. Usually such camouflage is effective against observation under visible light, particularly for wavelengths between 0.45 μm (micrometers) and 0.65 μm, and more particularly near 0.57 μm which corresponds to the maximum sensitivity of the human eye.
To successfully detect the intruding element, which is also called the “target” in the terminology of persons skilled in the art, despite a complex landscape and a possibly camouflaged target, it is known to perform a multispectral observation of the environment. Such multispectral observation consists of simultaneously capturing multiple images of the same landscape in different spectral bands, so that a target that does not clearly appear in images captured in some spectral bands is revealed by images corresponding to other spectral bands. Each spectral band may be narrow, with a wavelength range of a few tens of nanometers, or wider, possibly even very wide with a width of several micrometers, particularly when the spectral band is in one of the infrared domains: between 3 μm and 5 μm or between 8 μm and 12 μm. It is well known that observation in the wavelength range between 0.8 μm and 1.2 μm can be effective for revealing a target in an environment containing plant growth, when the target is effectively camouflaged against detection by observation within the light range visible to the human eye.
However, such multispectral detection may still not be sufficient to enable an operator responsible for surveillance to detect the presence of a target in a terrestrial environment. Indeed, in certain circumstances, none of the images separately associated with the spectral bands reveal the target clearly enough for the surveillance operator to detect the target in these images within the observation time allotted. Hereinafter, each image which separately corresponds to one of the spectral band is called the spectral image. For such situations, it is also known to improve the effectiveness of target detection by presenting the operator with an image constructed by Fisher projection. Such a process is known in particular from the article “Some practical issues in anomaly detection and exploitation of regions of interest in hyperspectral images” of Goudail F. et al., Applied Optics, Vol. 45, No. 21, pp. 5223-5236. According to this method, the image presented to the operator is constructed by combining at each dot of the image, called a pixel, the intensity values that were separately captured for a plurality of spectral bands, in order to optimize a contrast of the resulting image. Theoretically, this image construction consists of projecting for each pixel the vector of the intensities that have been captured for the spectral bands selected, in an optimal direction in the multidimensional space of the spectral intensity values. This optimal projection direction can be determined from the covariance matrix of the spectral intensities, estimated over the entire image field. In effect this means searching for a maximum correlation between the intensity variations that are present in the various images captured in the spectral bands selected. The contrast of the image presented to the operator is thus at least equal to that of each separate spectral image, so that target detection by the operator is both more efficient and more reliable. Alternatively, the optimal projection direction may be searched for directly by using a conventional optimization algorithm to maximize image contrast by varying the projection direction in the multidimensional space of the spectral intensities.
It is known to improve the contrast by Fisher projection within a spatial window which is smaller than the complete image. The surveillance operator selects the window within the complete image, particularly its position, based on the nature of the environment at that location in the image, and on his or her desire to intensify the search for a potential target in that area. To facilitate such a selection of the window position, and also to facilitate the identification and nature of an intrusion occurring in that area, it is also known to present the operator with a composite image on a screen. Such a composite image can be formed of one of the spectral images outside the window, and of the multispectral image portion resulting from the Fisher projection within the window. Alternatively, multiple spectral images having wavelength ranges within the range of sensitivity of the human eye can be used to display a representation of the landscape in natural or near-natural colors outside the window. However, in the composite image that is presented to the operator, the enhanced contrast provided by the Fisher projection is restricted to within the window. Because of this restriction, the surveillance operator does not have an enhanced-contrast visualization of the entire field of observation. He or she is therefore unable to quickly ascertain the extent of a camouflaged hostile intrusion, because of the time required to scan the entire field of observation with windows that must be successively selected and processed.